Overview

Dataset statistics

Number of variables11
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory91.6 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical2

Alerts

storm has constant value ""Constant
storm phase has constant value ""Constant
Dataset has 3 (< 0.1%) duplicate rowsDuplicates
400kmDensity is highly overall correlated with DAILY_SUNSPOT_NO_ and 4 other fieldsHigh correlation
DAILY_SUNSPOT_NO_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
DAILY_F10.7_ is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
SOLAR_LYMAN-ALPHA_W/m^2 is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
mg_index (core to wing ratio (unitless)) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
irradiance (W/m^2/nm) is highly overall correlated with 400kmDensity and 4 other fieldsHigh correlation
SYM/H_INDEX_nT is highly overall correlated with 1-M_AE_nTHigh correlation
1-M_AE_nT is highly overall correlated with SYM/H_INDEX_nTHigh correlation
SYM/H_INDEX_nT has 39814 (4.0%) zerosZeros
DAILY_SUNSPOT_NO_ has 318629 (31.9%) zerosZeros
d_diff has 13887 (1.4%) zerosZeros

Reproduction

Analysis started2023-02-24 21:39:43.252682
Analysis finished2023-02-24 21:40:31.119200
Duration47.87 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

400kmDensity
Real number (ℝ)

Distinct945211
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1479127 × 10-12
Minimum9.761599 × 10-17
Maximum1.456969 × 10-11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:40:31.197021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum9.761599 × 10-17
5-th percentile1.6635531 × 10-13
Q13.717014 × 10-13
median7.0593085 × 10-13
Q31.3696252 × 10-12
95-th percentile3.8342571 × 10-12
Maximum1.456969 × 10-11
Range1.4569592 × 10-11
Interquartile range (IQR)9.9792385 × 10-13

Descriptive statistics

Standard deviation1.2757371 × 10-12
Coefficient of variation (CV)1.1113538
Kurtosis0
Mean1.1479127 × 10-12
Median Absolute Deviation (MAD)4.0343635 × 10-13
Skewness0
Sum1.1479127 × 10-6
Variance1.6275052 × 10-24
MonotonicityNot monotonic
2023-02-24T16:40:31.321659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.281469 × 10-126
 
< 0.1%
1.083418 × 10-126
 
< 0.1%
1.267158 × 10-125
 
< 0.1%
1.276659 × 10-125
 
< 0.1%
1.167502 × 10-125
 
< 0.1%
1.069782 × 10-125
 
< 0.1%
3.772083 × 10-135
 
< 0.1%
1.022344 × 10-124
 
< 0.1%
1.201392 × 10-124
 
< 0.1%
1.521572 × 10-124
 
< 0.1%
Other values (945201) 999951
> 99.9%
ValueCountFrequency (%)
9.761599 × 10-171
< 0.1%
1.867288 × 10-161
< 0.1%
6.652488 × 10-161
< 0.1%
7.823372 × 10-161
< 0.1%
8.324271 × 10-161
< 0.1%
9.352196 × 10-161
< 0.1%
9.567154 × 10-162
< 0.1%
1.005823 × 10-151
< 0.1%
1.132964 × 10-151
< 0.1%
1.189884 × 10-151
< 0.1%
ValueCountFrequency (%)
1.456969 × 10-111
< 0.1%
1.405403 × 10-111
< 0.1%
1.329886 × 10-111
< 0.1%
1.274089 × 10-111
< 0.1%
1.229118 × 10-111
< 0.1%
1.206598 × 10-111
< 0.1%
1.202272 × 10-111
< 0.1%
1.201051 × 10-111
< 0.1%
1.200346 × 10-111
< 0.1%
1.198972 × 10-111
< 0.1%

SYM/H_INDEX_nT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct187
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.322739
Minimum-131
Maximum55
Zeros39814
Zeros (%)4.0%
Negative694321
Negative (%)69.4%
Memory size15.3 MiB
2023-02-24T16:40:31.456327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-131
5-th percentile-22
Q1-10
median-4
Q31
95-th percentile9
Maximum55
Range186
Interquartile range (IQR)11

Descriptive statistics

Standard deviation10.393404
Coefficient of variation (CV)-1.952642
Kurtosis7.5522205
Mean-5.322739
Median Absolute Deviation (MAD)6
Skewness-1.3151747
Sum-5322739
Variance108.02284
MonotonicityNot monotonic
2023-02-24T16:40:31.579999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3 52863
 
5.3%
-2 51418
 
5.1%
-4 49329
 
4.9%
-1 48366
 
4.8%
-5 47524
 
4.8%
-6 45536
 
4.6%
-7 44045
 
4.4%
1 41169
 
4.1%
-8 40421
 
4.0%
0 39814
 
4.0%
Other values (177) 539515
54.0%
ValueCountFrequency (%)
-131 1
 
< 0.1%
-130 5
< 0.1%
-129 1
 
< 0.1%
-128 3
 
< 0.1%
-127 3
 
< 0.1%
-126 6
< 0.1%
-125 8
< 0.1%
-124 2
 
< 0.1%
-123 4
 
< 0.1%
-122 10
< 0.1%
ValueCountFrequency (%)
55 4
 
< 0.1%
54 3
 
< 0.1%
53 7
< 0.1%
52 7
< 0.1%
51 5
< 0.1%
50 5
< 0.1%
49 3
 
< 0.1%
48 10
< 0.1%
47 6
< 0.1%
46 8
< 0.1%

1-M_AE_nT
Real number (ℝ)

Distinct1515
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.66864
Minimum1
Maximum2424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:40:31.708626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q129
median54
Q3130
95-th percentile392
Maximum2424
Range2423
Interquartile range (IQR)101

Descriptive statistics

Standard deviation138.11771
Coefficient of variation (CV)1.2709988
Kurtosis13.225513
Mean108.66864
Median Absolute Deviation (MAD)33
Skewness2.9931471
Sum1.0866864 × 108
Variance19076.502
MonotonicityNot monotonic
2023-02-24T16:40:31.833291image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 12209
 
1.2%
27 12100
 
1.2%
29 12093
 
1.2%
31 12017
 
1.2%
28 11980
 
1.2%
30 11980
 
1.2%
33 11895
 
1.2%
25 11856
 
1.2%
23 11802
 
1.2%
32 11784
 
1.2%
Other values (1505) 880284
88.0%
ValueCountFrequency (%)
1 31
 
< 0.1%
2 271
 
< 0.1%
3 990
 
0.1%
4 2196
 
0.2%
5 3467
0.3%
6 4942
0.5%
7 6159
0.6%
8 7276
0.7%
9 8109
0.8%
10 8651
0.9%
ValueCountFrequency (%)
2424 1
< 0.1%
2358 1
< 0.1%
2285 1
< 0.1%
2283 1
< 0.1%
2259 1
< 0.1%
2228 1
< 0.1%
2214 1
< 0.1%
2197 1
< 0.1%
2105 1
< 0.1%
2062 1
< 0.1%

DAILY_SUNSPOT_NO_
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct175
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.861747
Minimum0
Maximum281
Zeros318629
Zeros (%)31.9%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:40:31.955991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19
Q355
95-th percentile141
Maximum281
Range281
Interquartile range (IQR)55

Descriptive statistics

Standard deviation45.470105
Coefficient of variation (CV)1.2335309
Kurtosis1.9336643
Mean36.861747
Median Absolute Deviation (MAD)19
Skewness1.5563326
Sum36861747
Variance2067.5304
MonotonicityNot monotonic
2023-02-24T16:40:32.071655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 318629
31.9%
12 31187
 
3.1%
14 26346
 
2.6%
15 20303
 
2.0%
13 19053
 
1.9%
26 14873
 
1.5%
18 14779
 
1.5%
16 13687
 
1.4%
24 13052
 
1.3%
17 12277
 
1.2%
Other values (165) 515814
51.6%
ValueCountFrequency (%)
0 318629
31.9%
5 1379
 
0.1%
6 1819
 
0.2%
7 6918
 
0.7%
8 5592
 
0.6%
9 5766
 
0.6%
10 10309
 
1.0%
11 11772
 
1.2%
12 31187
 
3.1%
13 19053
 
1.9%
ValueCountFrequency (%)
281 81
 
< 0.1%
270 112
 
< 0.1%
207 699
0.1%
206 526
 
0.1%
202 860
0.1%
200 699
0.1%
199 1515
0.2%
195 664
0.1%
194 1380
0.1%
192 1102
0.1%

DAILY_F10.7_
Real number (ℝ)

Distinct609
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.3837
Minimum65.2
Maximum999.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:40:32.481586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum65.2
5-th percentile67.2
Q169.9
median77.3
Q398.5
95-th percentile145.1
Maximum999.9
Range934.7
Interquartile range (IQR)28.6

Descriptive statistics

Standard deviation36.857164
Coefficient of variation (CV)0.41234771
Kurtosis277.48311
Mean89.3837
Median Absolute Deviation (MAD)9
Skewness12.04476
Sum89383700
Variance1358.4505
MonotonicityNot monotonic
2023-02-24T16:40:32.607251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.4 11761
 
1.2%
69.8 10052
 
1.0%
69.5 9801
 
1.0%
70.2 9782
 
1.0%
68 9689
 
1.0%
68.6 9568
 
1.0%
67.8 9001
 
0.9%
68.2 8963
 
0.9%
70.3 8660
 
0.9%
68.8 8468
 
0.8%
Other values (599) 904255
90.4%
ValueCountFrequency (%)
65.2 681
 
0.1%
65.5 705
 
0.1%
65.6 674
 
0.1%
65.8 1343
0.1%
65.9 707
 
0.1%
66 2095
0.2%
66.1 1872
0.2%
66.2 2859
0.3%
66.3 2723
0.3%
66.4 1557
0.2%
ValueCountFrequency (%)
999.9 747
0.1%
254 703
0.1%
246.9 112
 
< 0.1%
240.6 728
0.1%
232.3 81
 
< 0.1%
223.5 382
 
< 0.1%
219 1368
0.1%
215.1 683
0.1%
209.2 696
0.1%
208.6 669
0.1%

SOLAR_LYMAN-ALPHA_W/m^2
Real number (ℝ)

Distinct997
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0066383124
Minimum0.00588
Maximum0.009662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:40:32.730919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.00588
5-th percentile0.00597
Q10.006073
median0.006376
Q30.007059
95-th percentile0.008082
Maximum0.009662
Range0.003782
Interquartile range (IQR)0.000986

Descriptive statistics

Standard deviation0.00071618437
Coefficient of variation (CV)0.10788651
Kurtosis1.6893119
Mean0.0066383124
Median Absolute Deviation (MAD)0.000355
Skewness1.3977093
Sum6638.3124
Variance5.1292005 × 10-7
MonotonicityNot monotonic
2023-02-24T16:40:32.857582image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00602 5795
 
0.6%
0.006026 4972
 
0.5%
0.006006 4886
 
0.5%
0.006005 4875
 
0.5%
0.005978 4337
 
0.4%
0.006047 4198
 
0.4%
0.006027 4122
 
0.4%
0.005965 3915
 
0.4%
0.006071 3864
 
0.4%
0.006035 3733
 
0.4%
Other values (987) 955303
95.5%
ValueCountFrequency (%)
0.00588 700
 
0.1%
0.005897 668
 
0.1%
0.005904 704
 
0.1%
0.005907 1395
0.1%
0.005909 699
 
0.1%
0.00591 2068
0.2%
0.005913 698
 
0.1%
0.005921 718
 
0.1%
0.005922 675
 
0.1%
0.005923 698
 
0.1%
ValueCountFrequency (%)
0.009662 112
 
< 0.1%
0.009577 81
 
< 0.1%
0.009555 683
0.1%
0.00954 696
0.1%
0.009511 669
0.1%
0.009451 696
0.1%
0.009429 382
< 0.1%
0.009281 669
0.1%
0.009187 526
0.1%
0.009174 719
0.1%
Distinct1384
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26776007
Minimum0.26295999
Maximum0.28428999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:40:33.002194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.26295999
5-th percentile0.26366001
Q10.26449577
median0.26596999
Q30.27006999
95-th percentile0.27737001
Maximum0.28428999
Range0.02133
Interquartile range (IQR)0.00557422

Descriptive statistics

Standard deviation0.0044249747
Coefficient of variation (CV)0.016525894
Kurtosis1.150499
Mean0.26776007
Median Absolute Deviation (MAD)0.00174174
Skewness1.3609241
Sum267760.07
Variance1.9580401 × 10-5
MonotonicityNot monotonic
2023-02-24T16:40:33.124869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26550001 3505
 
0.4%
0.26403001 3120
 
0.3%
0.26532999 2962
 
0.3%
0.26456001 2795
 
0.3%
0.26370001 2793
 
0.3%
0.26697999 2732
 
0.3%
0.26504001 2435
 
0.2%
0.27076 2169
 
0.2%
0.26585001 2154
 
0.2%
0.26368999 2153
 
0.2%
Other values (1374) 973182
97.3%
ValueCountFrequency (%)
0.26295999 710
0.1%
0.26299 491
< 0.1%
0.26300001 690
0.1%
0.26304999 680
0.1%
0.26306999 734
0.1%
0.26308 705
0.1%
0.26309001 689
0.1%
0.26311001 732
0.1%
0.26313001 698
0.1%
0.26315001 676
0.1%
ValueCountFrequency (%)
0.28428999 112
 
< 0.1%
0.2841 81
 
< 0.1%
0.28386 683
0.1%
0.28376999 696
0.1%
0.28373272 731
0.1%
0.28373 382
< 0.1%
0.28354329 687
0.1%
0.28353 669
0.1%
0.28325183 721
0.1%
0.28286999 696
0.1%

irradiance (W/m^2/nm)
Real number (ℝ)

Distinct1577
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0053788614
Minimum0.0048730583
Maximum0.0072682244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.3 MiB
2023-02-24T16:40:33.258526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0048730583
5-th percentile0.0049157543
Q10.0049798996
median0.0051998734
Q30.0056983922
95-th percentile0.0063578989
Maximum0.0072682244
Range0.0023951661
Interquartile range (IQR)0.00071849255

Descriptive statistics

Standard deviation0.00049714811
Coefficient of variation (CV)0.092426274
Kurtosis1.5407822
Mean0.0053788614
Median Absolute Deviation (MAD)0.00025128201
Skewness1.3641038
Sum5378.8614
Variance2.4715625 × 10-7
MonotonicityNot monotonic
2023-02-24T16:40:33.386172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004914534744 2152
 
0.2%
0.00495163817 2135
 
0.2%
0.004962376785 1482
 
0.1%
0.004942181055 1447
 
0.1%
0.004945096094 1446
 
0.1%
0.004921881482 1433
 
0.1%
0.004945786204 1429
 
0.1%
0.0049397205 1424
 
0.1%
0.00491212029 1420
 
0.1%
0.004950562492 1415
 
0.1%
Other values (1567) 984217
98.4%
ValueCountFrequency (%)
0.004873058293 692
0.1%
0.004877128173 677
0.1%
0.004877185915 715
0.1%
0.004877588246 686
0.1%
0.004881698173 357
< 0.1%
0.004881755915 698
0.1%
0.004885710776 721
0.1%
0.004885739647 715
0.1%
0.00488602696 359
< 0.1%
0.004886141978 720
0.1%
ValueCountFrequency (%)
0.007268224377 672
0.1%
0.007259562146 698
0.1%
0.007257604506 193
 
< 0.1%
0.007247306872 685
0.1%
0.007218547165 685
0.1%
0.007202866022 674
0.1%
0.007178029511 357
< 0.1%
0.007172006648 329
< 0.1%
0.007171689067 721
0.1%
0.007170669734 53
 
< 0.1%

storm
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
-1
1000000 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000000
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 1000000
100.0%

Length

2023-02-24T16:40:33.494879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:40:33.585634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
- 1000000
50.0%
1 1000000
50.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 1000000
50.0%
Decimal Number 1000000
50.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 1000000
100.0%
Decimal Number
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1000000
50.0%
1 1000000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1000000
50.0%
1 1000000
50.0%

storm phase
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
-1
1000000 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000000
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-1
2nd row-1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
-1 1000000
100.0%

Length

2023-02-24T16:40:33.657442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-24T16:40:33.749197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
- 1000000
50.0%
1 1000000
50.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 1000000
50.0%
Decimal Number 1000000
50.0%

Most frequent character per category

Dash Punctuation
ValueCountFrequency (%)
- 1000000
100.0%
Decimal Number
ValueCountFrequency (%)
1 1000000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1000000
50.0%
1 1000000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2000000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1000000
50.0%
1 1000000
50.0%

d_diff
Real number (ℝ)

Distinct848092
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.8234143 × 10-16
Minimum-4.400012 × 10-12
Maximum4.527286 × 10-12
Zeros13887
Zeros (%)1.4%
Negative481450
Negative (%)48.1%
Memory size15.3 MiB
2023-02-24T16:40:33.844941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4.400012 × 10-12
5-th percentile-1.2277705 × 10-13
Q1-2.951505 × 10-14
median4.545 × 10-16
Q33.084635 × 10-14
95-th percentile1.1756705 × 10-13
Maximum4.527286 × 10-12
Range8.927298 × 10-12
Interquartile range (IQR)6.03614 × 10-14

Descriptive statistics

Standard deviation1.0273436 × 10-13
Coefficient of variation (CV)-563.41752
Kurtosis0
Mean-1.8234143 × 10-16
Median Absolute Deviation (MAD)3.0191855 × 10-14
Skewness0
Sum-1.8234143 × 10-10
Variance1.0554348 × 10-26
MonotonicityNot monotonic
2023-02-24T16:40:33.964594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13887
 
1.4%
-6.006 × 10-159
 
< 0.1%
5.2847 × 10-149
 
< 0.1%
-1.0246 × 10-149
 
< 0.1%
-1.102 × 10-159
 
< 0.1%
-5.95 × 10-158
 
< 0.1%
4.063 × 10-158
 
< 0.1%
-1.5404 × 10-148
 
< 0.1%
7.928 × 10-158
 
< 0.1%
-5.498 × 10-158
 
< 0.1%
Other values (848082) 986037
98.6%
ValueCountFrequency (%)
-4.400012 × 10-121
< 0.1%
-4.1894118 × 10-121
< 0.1%
-3.971038 × 10-121
< 0.1%
-3.565951 × 10-121
< 0.1%
-3.560486 × 10-121
< 0.1%
-3.540558 × 10-121
< 0.1%
-3.462188 × 10-121
< 0.1%
-3.225641 × 10-121
< 0.1%
-3.120329 × 10-121
< 0.1%
-3.105367 × 10-121
< 0.1%
ValueCountFrequency (%)
4.527286 × 10-121
< 0.1%
4.334026 × 10-121
< 0.1%
3.873266 × 10-121
< 0.1%
3.834684 × 10-121
< 0.1%
3.485882 × 10-121
< 0.1%
3.28273 × 10-121
< 0.1%
3.035233 × 10-121
< 0.1%
3.019337 × 10-121
< 0.1%
2.958724 × 10-121
< 0.1%
2.937913 × 10-121
< 0.1%

Interactions

2023-02-24T16:40:27.761209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:12.653596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:14.546535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:16.665842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:18.498979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:20.334066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:22.144195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:24.064093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:25.915114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:27.963640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:12.868993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:14.758969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:16.881298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:18.713367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:20.542478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:22.364636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:24.275497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:26.129568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:28.165130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:13.084444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:14.970402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:17.087747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:18.924804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:20.751945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:22.586045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:24.490923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:26.341974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:28.353622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:13.289872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:15.174830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:17.282227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:19.121309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:20.949418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:22.796451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:24.693380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:26.545457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:28.555056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:13.500304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:15.397266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:17.484680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:19.317752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:21.145899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:23.010907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:24.896837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:26.747919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:28.742556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:13.705783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:15.600717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:17.681156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:19.516251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:21.337399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:23.213365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:25.098326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:26.944392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:28.948040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:13.929158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:15.823122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:17.894585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:19.730685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:21.548815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:23.430783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:25.305773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:27.158791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:29.145511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:14.137602image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:16.029573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:18.098043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:19.937136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:21.748253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:23.643191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:25.509199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:27.354271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:29.330023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:14.344077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:16.461416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:18.298506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:20.138585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:21.946760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:23.852661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:25.711690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-24T16:40:27.551775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-24T16:40:34.064328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diff
400kmDensity1.000-0.1790.3100.7360.7870.8170.7770.8230.048
SYM/H_INDEX_nT-0.1791.000-0.386-0.062-0.082-0.098-0.068-0.099-0.003
1-M_AE_nT0.310-0.3861.0000.2650.2830.3050.2420.3090.005
DAILY_SUNSPOT_NO_0.736-0.0620.2651.0000.9180.8870.8640.8770.007
DAILY_F10.7_0.787-0.0820.2830.9181.0000.9490.9270.9460.008
SOLAR_LYMAN-ALPHA_W/m^20.817-0.0980.3050.8870.9491.0000.9260.9910.008
mg_index (core to wing ratio (unitless))0.777-0.0680.2420.8640.9270.9261.0000.9200.005
irradiance (W/m^2/nm)0.823-0.0990.3090.8770.9460.9910.9201.0000.008
d_diff0.048-0.0030.0050.0070.0080.0080.0050.0081.000
2023-02-24T16:40:34.228918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.2380.2660.7650.5940.8470.8170.849NaNNaN0.038
SYM/H_INDEX_nT-0.2381.000-0.486-0.099-0.125-0.179-0.137-0.173NaNNaN0.001
1-M_AE_nT0.266-0.4861.0000.1930.1510.2510.1940.254NaNNaN0.002
DAILY_SUNSPOT_NO_0.765-0.0990.1931.0000.6730.9110.8970.896NaNNaN-0.002
DAILY_F10.7_0.594-0.1250.1510.6731.0000.6830.6710.674NaNNaN-0.001
SOLAR_LYMAN-ALPHA_W/m^20.847-0.1790.2510.9110.6831.0000.9610.994NaNNaN-0.002
mg_index (core to wing ratio (unitless))0.817-0.1370.1940.8970.6710.9611.0000.954NaNNaN-0.002
irradiance (W/m^2/nm)0.849-0.1730.2540.8960.6740.9940.9541.000NaNNaN-0.002
stormNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
storm phaseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
d_diff0.0380.0010.002-0.002-0.001-0.002-0.002-0.002NaNNaN1.000
2023-02-24T16:40:34.408406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.1790.3100.7360.7870.8170.7770.823NaNNaN0.048
SYM/H_INDEX_nT-0.1791.000-0.386-0.062-0.082-0.098-0.068-0.099NaNNaN-0.003
1-M_AE_nT0.310-0.3861.0000.2650.2830.3050.2420.309NaNNaN0.005
DAILY_SUNSPOT_NO_0.736-0.0620.2651.0000.9180.8870.8640.877NaNNaN0.007
DAILY_F10.7_0.787-0.0820.2830.9181.0000.9490.9270.946NaNNaN0.008
SOLAR_LYMAN-ALPHA_W/m^20.817-0.0980.3050.8870.9491.0000.9260.991NaNNaN0.008
mg_index (core to wing ratio (unitless))0.777-0.0680.2420.8640.9270.9261.0000.920NaNNaN0.005
irradiance (W/m^2/nm)0.823-0.0990.3090.8770.9460.9910.9201.000NaNNaN0.008
stormNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
storm phaseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
d_diff0.048-0.0030.0050.0070.0080.0080.0050.008NaNNaN1.000
2023-02-24T16:40:34.584965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
400kmDensity1.000-0.1240.2090.5520.5840.6160.5740.622NaNNaN0.035
SYM/H_INDEX_nT-0.1241.000-0.267-0.044-0.056-0.067-0.046-0.068NaNNaN-0.002
1-M_AE_nT0.209-0.2671.0000.1850.1920.2070.1620.209NaNNaN0.004
DAILY_SUNSPOT_NO_0.552-0.0440.1851.0000.7730.7220.6940.706NaNNaN0.005
DAILY_F10.7_0.584-0.0560.1920.7731.0000.8050.7670.796NaNNaN0.005
SOLAR_LYMAN-ALPHA_W/m^20.616-0.0670.2070.7220.8051.0000.7680.923NaNNaN0.006
mg_index (core to wing ratio (unitless))0.574-0.0460.1620.6940.7670.7681.0000.757NaNNaN0.003
irradiance (W/m^2/nm)0.622-0.0680.2090.7060.7960.9230.7571.000NaNNaN0.006
stormNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaN
storm phaseNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN
d_diff0.035-0.0020.0040.0050.0050.0060.0030.006NaNNaN1.000
2023-02-24T16:40:34.760493image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)d_diff
400kmDensity1.0000.3780.3800.5780.5620.7940.7670.7890.361
SYM/H_INDEX_nT0.3781.0000.5020.2010.2580.3960.3020.3670.147
1-M_AE_nT0.3800.5021.0000.1520.1120.2520.2040.2480.127
DAILY_SUNSPOT_NO_0.5780.2010.1521.0000.6500.7470.7240.7230.174
DAILY_F10.7_0.5620.2580.1120.6501.0000.6660.6270.6360.136
SOLAR_LYMAN-ALPHA_W/m^20.7940.3960.2520.7470.6661.0000.9360.9730.272
mg_index (core to wing ratio (unitless))0.7670.3020.2040.7240.6270.9361.0000.9210.255
irradiance (W/m^2/nm)0.7890.3670.2480.7230.6360.9730.9211.0000.273
d_diff0.3610.1470.1270.1740.1360.2720.2550.2731.000

Missing values

2023-02-24T16:40:29.475623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-24T16:40:29.983266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
12309678.459346e-13-1.085.012.092.70.0069430.2709060.005652-1-11.411840e-14
24919391.031814e-13-3.049.00.070.60.0060080.2646710.004940-1-1-1.075160e-14
10608642.291023e-132.0216.015.075.20.0063950.2646300.005240-1-1-3.347330e-14
31839474.540360e-135.028.00.067.30.0060790.2637300.004988-1-18.248980e-14
45708441.540450e-128.033.066.086.60.0067620.2671900.005468-1-1-4.061410e-13
28404303.282664e-13-3.054.00.069.00.0061190.2636500.005010-1-16.750780e-14
1484822.622863e-132.026.00.069.80.0059950.2645360.004934-1-18.581900e-15
27074423.876435e-13-2.025.010.075.50.0061570.2657440.005028-1-19.817300e-15
38514381.031428e-12-14.019.00.074.50.0064190.2650500.005227-1-15.595430e-14
3342629.704644e-13-7.030.07.069.50.0059400.2643300.004904-1-1-1.381700e-14
400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff
44300545.620432e-133.0154.040.082.50.0065450.2665400.005332-1-11.286660e-14
12476281.603944e-12-5.014.077.0106.00.0073080.2738940.005848-1-16.466400e-14
45709611.015021e-125.079.066.086.60.0067620.2671900.005468-1-1-7.140400e-14
18179322.460156e-13-1.0210.00.067.10.0059380.2634410.004887-1-13.700390e-14
24580104.233585e-130.032.00.074.40.0064160.2653900.005245-1-1-1.024640e-14
6230945.680872e-1311.010.012.069.30.0059990.2648300.004948-1-11.357139e-13
9519154.070535e-13-9.0112.00.068.10.0059100.2634460.004886-1-1-1.448035e-13
1448232.498747e-13-4.030.00.069.80.0059840.2644150.004943-1-11.838528e-13
31886303.822389e-13-3.021.00.066.70.0060540.2639800.004962-1-12.538260e-14
21320696.166821e-13-7.050.012.073.90.0062440.2641800.005118-1-14.920610e-14

Duplicate rows

Most frequently occurring

400kmDensitySYM/H_INDEX_nT1-M_AE_nTDAILY_SUNSPOT_NO_DAILY_F10.7_SOLAR_LYMAN-ALPHA_W/m^2mg_index (core to wing ratio (unitless))irradiance (W/m^2/nm)stormstorm phased_diff# duplicates
01.294546e-13-1.034.00.071.20.0061340.2649080.005012-1-10.02
13.303634e-130.086.00.068.20.0059310.2637570.004896-1-10.02
24.415150e-130.034.017.070.20.0059990.2648100.004944-1-10.02